Overview

Dataset statistics

Number of variables20
Number of observations3188
Missing cells0
Missing cells (%)0.0%
Duplicate rows333
Duplicate rows (%)10.4%
Total size in memory498.2 KiB
Average record size in memory160.0 B

Variable types

NUM13
CAT7

Reproduction

Analysis started2020-08-25 01:56:34.824064
Analysis finished2020-08-25 01:57:04.770883
Duration29.95 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 333 (10.4%) duplicate rows Duplicates
A0 has 747 (23.4%) zeros Zeros
A36 has 814 (25.5%) zeros Zeros
A45 has 757 (23.7%) zeros Zeros
A13 has 803 (25.2%) zeros Zeros
A54 has 799 (25.1%) zeros Zeros
A33 has 1116 (35.0%) zeros Zeros
A48 has 723 (22.7%) zeros Zeros
A57 has 724 (22.7%) zeros Zeros
A46 has 730 (22.9%) zeros Zeros
A50 has 697 (21.9%) zeros Zeros
A31 has 596 (18.7%) zeros Zeros
A52 has 734 (23.0%) zeros Zeros
A40 has 722 (22.6%) zeros Zeros

Variables

A0
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0075282308657467
Minimum0
Maximum4
Zeros747
Zeros (%)23.4%
Memory size25.0 KiB
2020-08-25T01:57:04.820807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.547878222
Coefficient of variation (CV)0.7710368396
Kurtosis-1.609878221
Mean2.007528231
Median Absolute Deviation (MAD)1
Skewness-0.01815575205
Sum6400
Variance2.395926992
2020-08-25T01:57:04.926884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
387527.4%
 
182926.0%
 
074723.4%
 
473623.1%
 
21< 0.1%
 
ValueCountFrequency (%) 
074723.4%
 
182926.0%
 
21< 0.1%
 
387527.4%
 
473623.1%
 
ValueCountFrequency (%) 
473623.1%
 
387527.4%
 
21< 0.1%
 
182926.0%
 
074723.4%
 

A5
Categorical

Distinct count4
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
1
901
2
821
3
754
0
712
ValueCountFrequency (%) 
190128.3%
 
282125.8%
 
375423.7%
 
071222.3%
 
2020-08-25T01:57:05.082197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
190128.3%
 
282125.8%
 
375423.7%
 
071222.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
190128.3%
 
282125.8%
 
375423.7%
 
071222.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
190128.3%
 
282125.8%
 
375423.7%
 
071222.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
190128.3%
 
282125.8%
 
375423.7%
 
071222.3%
 

A36
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.679422835633626
Minimum0
Maximum4
Zeros814
Zeros (%)25.5%
Memory size25.0 KiB
2020-08-25T01:57:05.198181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.411145084
Coefficient of variation (CV)0.840255982
Kurtosis-0.935443104
Mean1.679422836
Median Absolute Deviation (MAD)1
Skewness0.4829631137
Sum5354
Variance1.991330448
2020-08-25T01:57:05.306273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
296630.3%
 
081425.5%
 
173623.1%
 
467021.0%
 
320.1%
 
ValueCountFrequency (%) 
081425.5%
 
173623.1%
 
296630.3%
 
320.1%
 
467021.0%
 
ValueCountFrequency (%) 
467021.0%
 
320.1%
 
296630.3%
 
173623.1%
 
081425.5%
 

A45
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7057716436637391
Minimum0
Maximum4
Zeros757
Zeros (%)23.7%
Memory size25.0 KiB
2020-08-25T01:57:05.436071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.42063481
Coefficient of variation (CV)0.8328399733
Kurtosis-0.9753252616
Mean1.705771644
Median Absolute Deviation (MAD)1
Skewness0.4963578294
Sum5438
Variance2.018203264
2020-08-25T01:57:05.540145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
287827.5%
 
184326.4%
 
075723.7%
 
470922.2%
 
31< 0.1%
 
ValueCountFrequency (%) 
075723.7%
 
184326.4%
 
287827.5%
 
31< 0.1%
 
470922.2%
 
ValueCountFrequency (%) 
470922.2%
 
31< 0.1%
 
287827.5%
 
184326.4%
 
075723.7%
 

A13
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6994981179422837
Minimum0
Maximum4
Zeros803
Zeros (%)25.2%
Memory size25.0 KiB
2020-08-25T01:57:05.662057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.472700986
Coefficient of variation (CV)0.8665505249
Kurtosis-1.094942119
Mean1.699498118
Median Absolute Deviation (MAD)1
Skewness0.5085788896
Sum5418
Variance2.168848195
2020-08-25T01:57:05.767263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
189828.2%
 
080325.2%
 
477224.2%
 
271322.4%
 
320.1%
 
ValueCountFrequency (%) 
080325.2%
 
189828.2%
 
271322.4%
 
320.1%
 
477224.2%
 
ValueCountFrequency (%) 
477224.2%
 
320.1%
 
271322.4%
 
189828.2%
 
080325.2%
 

A54
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6828732747804267
Minimum0
Maximum4
Zeros799
Zeros (%)25.1%
Memory size25.0 KiB
2020-08-25T01:57:05.884277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.431018701
Coefficient of variation (CV)0.8503425196
Kurtosis-0.9824211661
Mean1.682873275
Median Absolute Deviation (MAD)1
Skewness0.5080343702
Sum5365
Variance2.047814522
2020-08-25T01:57:06.008793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
285626.9%
 
182625.9%
 
079925.1%
 
470622.1%
 
31< 0.1%
 
ValueCountFrequency (%) 
079925.1%
 
182625.9%
 
285626.9%
 
31< 0.1%
 
470622.1%
 
ValueCountFrequency (%) 
470622.1%
 
31< 0.1%
 
285626.9%
 
182625.9%
 
079925.1%
 

A33
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4523212045169385
Minimum0
Maximum4
Zeros1116
Zeros (%)35.0%
Memory size25.0 KiB
2020-08-25T01:57:06.135370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.442959257
Coefficient of variation (CV)0.9935538034
Kurtosis-0.7962542722
Mean1.452321205
Median Absolute Deviation (MAD)1
Skewness0.6893532309
Sum4630
Variance2.082131416
2020-08-25T01:57:06.241355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0111635.0%
 
275023.5%
 
171922.6%
 
460218.9%
 
31< 0.1%
 
ValueCountFrequency (%) 
0111635.0%
 
171922.6%
 
275023.5%
 
31< 0.1%
 
460218.9%
 
ValueCountFrequency (%) 
460218.9%
 
31< 0.1%
 
275023.5%
 
171922.6%
 
0111635.0%
 

A48
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6941656210790463
Minimum0
Maximum4
Zeros723
Zeros (%)22.7%
Memory size25.0 KiB
2020-08-25T01:57:06.361849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.384708669
Coefficient of variation (CV)0.8173396107
Kurtosis-0.8702551444
Mean1.694165621
Median Absolute Deviation (MAD)1
Skewness0.5166374184
Sum5401
Variance1.917418099
2020-08-25T01:57:06.468738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
294029.5%
 
185926.9%
 
072322.7%
 
466420.8%
 
320.1%
 
ValueCountFrequency (%) 
072322.7%
 
185926.9%
 
294029.5%
 
320.1%
 
466420.8%
 
ValueCountFrequency (%) 
466420.8%
 
320.1%
 
294029.5%
 
185926.9%
 
072322.7%
 

A12
Categorical

Distinct count4
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
1
876
3
826
2
759
0
727
ValueCountFrequency (%) 
187627.5%
 
382625.9%
 
275923.8%
 
072722.8%
 
2020-08-25T01:57:06.617168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
187627.5%
 
382625.9%
 
275923.8%
 
072722.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
187627.5%
 
382625.9%
 
275923.8%
 
072722.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
187627.5%
 
382625.9%
 
275923.8%
 
072722.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
187627.5%
 
382625.9%
 
275923.8%
 
072722.8%
 

A57
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7368255959849435
Minimum0
Maximum4
Zeros724
Zeros (%)22.7%
Memory size25.0 KiB
2020-08-25T01:57:06.906088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.429681843
Coefficient of variation (CV)0.8231579762
Kurtosis-1.024474791
Mean1.736825596
Median Absolute Deviation (MAD)1
Skewness0.4796426465
Sum5537
Variance2.043990171
2020-08-25T01:57:07.010774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
187427.4%
 
284826.6%
 
474123.2%
 
072422.7%
 
31< 0.1%
 
ValueCountFrequency (%) 
072422.7%
 
187427.4%
 
284826.6%
 
31< 0.1%
 
474123.2%
 
ValueCountFrequency (%) 
474123.2%
 
31< 0.1%
 
284826.6%
 
187427.4%
 
072422.7%
 

A46
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7854454203262233
Minimum0
Maximum4
Zeros730
Zeros (%)22.9%
Memory size25.0 KiB
2020-08-25T01:57:07.129442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.440509953
Coefficient of variation (CV)0.8068070505
Kurtosis-1.089383931
Mean1.78544542
Median Absolute Deviation (MAD)1
Skewness0.4031516818
Sum5692
Variance2.075068926
2020-08-25T01:57:07.233598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
292529.0%
 
476924.1%
 
176323.9%
 
073022.9%
 
31< 0.1%
 
ValueCountFrequency (%) 
073022.9%
 
176323.9%
 
292529.0%
 
31< 0.1%
 
476924.1%
 
ValueCountFrequency (%) 
476924.1%
 
31< 0.1%
 
292529.0%
 
176323.9%
 
073022.9%
 

A50
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.730865746549561
Minimum0
Maximum4
Zeros697
Zeros (%)21.9%
Memory size25.0 KiB
2020-08-25T01:57:07.355366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.402749867
Coefficient of variation (CV)0.8104325073
Kurtosis-0.9498822309
Mean1.730865747
Median Absolute Deviation (MAD)1
Skewness0.4929266357
Sum5518
Variance1.967707189
2020-08-25T01:57:07.460947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
290128.3%
 
188127.6%
 
470822.2%
 
069721.9%
 
31< 0.1%
 
ValueCountFrequency (%) 
069721.9%
 
188127.6%
 
290128.3%
 
31< 0.1%
 
470822.2%
 
ValueCountFrequency (%) 
470822.2%
 
31< 0.1%
 
290128.3%
 
188127.6%
 
069721.9%
 

A31
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.342220828105395
Minimum0
Maximum4
Zeros596
Zeros (%)18.7%
Memory size25.0 KiB
2020-08-25T01:57:07.580366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.61414724
Coefficient of variation (CV)0.6891524578
Kurtosis-1.585411065
Mean2.342220828
Median Absolute Deviation (MAD)2
Skewness-0.1914018206
Sum7467
Variance2.605471314
2020-08-25T01:57:07.691009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4142944.8%
 
059618.7%
 
258618.4%
 
157618.1%
 
31< 0.1%
 
ValueCountFrequency (%) 
059618.7%
 
157618.1%
 
258618.4%
 
31< 0.1%
 
4142944.8%
 
ValueCountFrequency (%) 
4142944.8%
 
31< 0.1%
 
258618.4%
 
157618.1%
 
059618.7%
 

A3
Categorical

Distinct count4
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
1
876
2
801
0
757
3
754
ValueCountFrequency (%) 
187627.5%
 
280125.1%
 
075723.7%
 
375423.7%
 
2020-08-25T01:57:07.849188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
187627.5%
 
280125.1%
 
075723.7%
 
375423.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
187627.5%
 
280125.1%
 
075723.7%
 
375423.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
187627.5%
 
280125.1%
 
075723.7%
 
375423.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
187627.5%
 
280125.1%
 
075723.7%
 
375423.7%
 

A52
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.751254705144291
Minimum0
Maximum4
Zeros734
Zeros (%)23.0%
Memory size25.0 KiB
2020-08-25T01:57:07.960161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.434015143
Coefficient of variation (CV)0.8188501298
Kurtosis-1.047750744
Mean1.751254705
Median Absolute Deviation (MAD)1
Skewness0.4504453481
Sum5583
Variance2.056399429
2020-08-25T01:57:08.065511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
288327.7%
 
182225.8%
 
474823.5%
 
073423.0%
 
31< 0.1%
 
ValueCountFrequency (%) 
073423.0%
 
182225.8%
 
288327.7%
 
31< 0.1%
 
474823.5%
 
ValueCountFrequency (%) 
474823.5%
 
31< 0.1%
 
288327.7%
 
182225.8%
 
073423.0%
 

A17
Categorical

Distinct count4
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
3
915
1
887
2
736
0
650
ValueCountFrequency (%) 
391528.7%
 
188727.8%
 
273623.1%
 
065020.4%
 
2020-08-25T01:57:08.219165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
391528.7%
 
188727.8%
 
273623.1%
 
065020.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
391528.7%
 
188727.8%
 
273623.1%
 
065020.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
391528.7%
 
188727.8%
 
273623.1%
 
065020.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
391528.7%
 
188727.8%
 
273623.1%
 
065020.4%
 

A8
Categorical

Distinct count4
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
1
900
3
797
0
752
2
739
ValueCountFrequency (%) 
190028.2%
 
379725.0%
 
075223.6%
 
273923.2%
 
2020-08-25T01:57:08.362359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
190028.2%
 
379725.0%
 
075223.6%
 
273923.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
190028.2%
 
379725.0%
 
075223.6%
 
273923.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
190028.2%
 
379725.0%
 
075223.6%
 
273923.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
190028.2%
 
379725.0%
 
075223.6%
 
273923.2%
 

A6
Categorical

Distinct count4
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
1
860
0
787
3
782
2
759
ValueCountFrequency (%) 
186027.0%
 
078724.7%
 
378224.5%
 
275923.8%
 
2020-08-25T01:57:08.508466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
186027.0%
 
078724.7%
 
378224.5%
 
275923.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
186027.0%
 
078724.7%
 
378224.5%
 
275923.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
186027.0%
 
078724.7%
 
378224.5%
 
275923.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
186027.0%
 
078724.7%
 
378224.5%
 
275923.8%
 

A40
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7754077791718945
Minimum0
Maximum4
Zeros722
Zeros (%)22.6%
Memory size25.0 KiB
2020-08-25T01:57:08.621286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.436796454
Coefficient of variation (CV)0.8092768721
Kurtosis-1.073609599
Mean1.775407779
Median Absolute Deviation (MAD)1
Skewness0.4233100258
Sum5660
Variance2.064384051
2020-08-25T01:57:08.725175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
290328.3%
 
179925.1%
 
476323.9%
 
072222.6%
 
31< 0.1%
 
ValueCountFrequency (%) 
072222.6%
 
179925.1%
 
290328.3%
 
31< 0.1%
 
476323.9%
 
ValueCountFrequency (%) 
476323.9%
 
31< 0.1%
 
290328.3%
 
179925.1%
 
072222.6%
 

target
Categorical

Distinct count3
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
2
1655
1
769
0
764
ValueCountFrequency (%) 
2165551.9%
 
176924.1%
 
076424.0%
 
2020-08-25T01:57:08.873640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
2165551.9%
 
176924.1%
 
076424.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3188100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2165551.9%
 
176924.1%
 
076424.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3188100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2165551.9%
 
176924.1%
 
076424.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2165551.9%
 
176924.1%
 
076424.0%
 

Interactions

2020-08-25T01:56:36.127585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:36.284510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:36.445056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:36.603508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:36.760771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:36.921544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:37.078968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:37.236611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:37.395836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:37.557729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:37.716625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:37.870625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:38.025936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:38.183420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:38.340957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:38.499320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:38.658757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.005473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.161139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.320850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.477715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.634568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.788718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:39.945874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:40.102509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:40.258343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:40.418037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:40.580845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:40.737236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:40.889883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.046670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.203073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.359417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.515678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.671009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.825755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:41.984636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:42.140631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:42.294468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:42.450228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:42.605996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:42.760322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:42.918313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:43.077677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:43.234764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:43.392973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:43.738822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:43.898409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:44.076656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:44.235474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:44.395705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:44.557599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:44.710292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:44.860737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.017838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.174128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.333990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.493050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.650754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.803209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:45.963633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:46.124936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:46.278647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:46.432921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:46.591230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:46.745346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:46.903318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.060255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.213877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.366923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.522383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.676311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.829540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:47.983284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:48.142221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:48.481500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:48.635372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:48.797850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:48.961733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:49.119047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:49.273446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:49.427657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:49.589052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:49.747599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:49.903407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:50.059285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:50.215638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:50.370266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:50.526972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:50.686008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:50.844361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.002227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.160439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.315204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.472242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.634500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.787107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:51.939265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:52.093044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:52.247893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:52.402280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:52.555099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:52.709449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:52.859714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:53.202181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:53.356543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:53.514862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:53.672477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:53.830050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:53.994876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:54.149626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:54.315158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:54.472642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:54.628422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:54.791385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:54.944861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:55.099882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:55.262949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:55.417841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:55.579606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:55.755147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:55.910674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:56.070266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:56.228574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:56.397117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:56.558509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:56.790240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:56.974714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:57.133811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:57.295738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:57.450274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:57.608429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:57.767394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:58.117610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:58.271060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:58.424727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:58.578503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:58.739829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:58.896829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:59.060776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:59.218032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:59.374577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:59.541711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:59.695400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:56:59.854563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.012536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.164928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.322907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.478891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.637086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.795682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:00.951304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:01.113736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:01.279199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:01.436574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:01.592570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:01.752222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:01.911802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:02.067725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:02.218825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:02.375811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:02.529750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:02.869436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:03.029855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:03.183609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:03.338362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:03.497017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:03.653246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:03.807136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:57:09.023093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:57:09.318869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:57:09.618050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:57:09.909003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-25T01:57:10.165678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:57:04.118746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:57:04.603497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

A0A5A36A45A13A54A33A48A12A57A46A50A31A3A52A17A8A6A40target
002120424244041431120
132144411112113231141
202241411124442422302
341200222221411111312
400100402042103100222
511421100102411121321
600441040041102133002
712411020110200211242
841021040100111033342
911002201044021411342

Last rows

A0A5A36A45A13A54A33A48A12A57A46A50A31A3A52A17A8A6A40target
317803212404221243103320
317912141422222411121112
318032412241224120232202
318131042212212242132220
318240001414020112111022
318342212440040122133122
318401221142212423023322
318541220101220140202020
318612411442104101433111
318741421441114441423100

Duplicate rows

Most frequent

A0A5A36A45A13A54A33A48A12A57A46A50A31A3A52A17A8A6A40targetcount
152312010113221212113018
140304122220201400013207
65102111103212130310216
105122240020121421002106
1000114223011030120415
63101210013222032312415
89114140221100412110215
122132142121442411333015
170322002013044414211105
172322111101121011101415